Substructural analysis. Novel approach to the problem of drug design

Cramer, Richard D.; Redl, G.; Berkoff, Charles E.

doi:10.1021/jm00251a014

Cited by 133 publications

(83 citation statements)

References 1 publication

(1 reference statement)

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“…These training-set molecules are then analysed to develop a decision rule that can be used to classify new molecules (the test-set) into one of the two classes. The first application of machine learning in computer-aided molecular design (CAMD) was probably substructural analysis, which was introduced by Cramer et al in the early Seventies as a tool for the automated analysis of biological screening data [18,19]. Machine learning is now a very active area of research in computer science, with the increasing availability of large data repositories of all sorts spurring interest in the development of novel tools for data mining [20,21].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of machine-learning methods for ligand-based virtual screening

Chen

Harrison

Papadatos

et al. 2007

J Comput Aided Mol Des

113

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Abstract. Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of a naive Bayesian classifier when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed.

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Section: Introductionmentioning

confidence: 99%

Evaluation of machine-learning methods for ligand-based virtual screening

Chen

Harrison

Papadatos

et al. 2007

J Comput Aided Mol Des

113

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“…6 Finally, if the 3D structure of the biological target is known, then a docking study can be carried out to identify those database molecules that are complementary to the binding site. 7 This paper reports a comparison of methods that can be used for the third class of virtual screening methods, which includes such common approaches as substructural analysis, 8,9 genetic algorithms, 10 neural networks, 11,12 and decision trees. [13][14][15] Two main classes of approach are possible: ranking methods order a database in order of decreasing probability of activity and classification methods divide a database into those molecules that are predicted to be active and those that are predicted to be inactive.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Ranking Methods for Virtual Screening in Lead-Discovery Programs

Wilton¹,

Willett²,

Lawson³

et al. 2002

J. Chem. Inf. Comput. Sci.

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This paper discusses the use of several rank-based virtual screening methods for prioritizing compounds in lead-discovery programs, given a training set for which both structural and bioactivity data are available. Structures from the NCI AIDS data set and from the Syngenta corporate database were represented by two types of fragment bit-string and by sets of high-level molecular features. These representations were processed using binary kernel discrimination, similarity searching, substructural analysis, support vector machine, and trend vector analysis, with the effectiveness of the methods being judged by the extent to which active test set molecules were clustered toward the top of the resultant rankings. The binary kernel discrimination approach yielded consistently superior rankings and would appear to have considerable potential for chemical screening applications.

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“…The GA seeks to identify those weights that produce the best possible ranking of the molecules in a dataset, and hence to estimate an upper-bound to the effectiveness of virtual screening possible using the substructural analysis approach. The basic idea is illustrated in Figure 1 using a training-set containing three molecules M 1-3 , each of which is represented by a fingerprint encoding the presence or absence of five fragments F [1][2][3][4][5] .…”

Section: The Genetic Algorithmmentioning

confidence: 99%

“…An initial population of possible solutions is generated with the initial weights W 1 -W 5 being assigned by a randomnumber generator that has been primed in this simple example to generate integer weights in the range 0-10. In the example, the population contains six chromosomes, C [1][2][3][4][5][6] , and the initial population is shown in Figure 1b. Each chromosome is then used to compute the sum-of-weights for each molecule, as shown in Figure 1c.…”

Section: The Genetic Algorithmmentioning

confidence: 99%